Cultural and Genetic Influences on the “Biological Standard of Living”

Abstract

This study suggests an improvement of a popular measure of living standards, namely the biological standard of living. One influence on it is a population's consumption pattern. Since there are different dietary patterns all over the world, researchers estimate the influences of national diets on final average male height. These habits are predominantly related to income, but also to genetics, cultural history, and decisions regarding whether to trade or consume high-quality foodstuffs. Systematic differences are found when analyzing protein-consumption habits among 51 countries between the 1960s and the 1980s. The author calculates metric correction values which can facilitate international comparisons of male average height. While the proposed correction values make a little difference on average, they can be valuable in a comparison of countries with markedly different dietary patterns.

Keywords :

• biological standard of living
• consumption
• height
• lactose intolerance
• animal protein
• dietary patterns

Notes

1. The biological standard of living is not only applied as a substitute for income. Some scholars apply it when conventional yardsticks are lacking. In my opinion, income reflects economic effectiveness and efficiency in terms of production. The economic behavior of people ensures that this production potential is used to produce all sorts of “well-being”: health, nutrition, leisure time, education, entertainment, etc. Some of these facets of well-being are responsible for a drop of mortality rates and the increase of average height. Neither of these measures is superior; each of them represents a separate way to look at well-being.

2. Table 3 lists major sources of calories and protein for each of the 20 clusters.

3http://www.mortality.org/.

4. Malay Peninsula, Myanmar, Thailand, Indo-China, Assam, and Bengal, as well as the northern part of sub-Saharan Africa (Simoons 1954, 1970).

5. On the other hand, the crucial growth period for final height is the first two to three years in a child's life. Therefore, differences in lactose prevalence matter mainly during this period; differences during later periods should not be neglected, but they exert only a weaker influence.

6. There is still a dispute among evolutionary biologists about the reason for the differences in the frequency of occurrence between populations, but the most popular theories emphasize an evolutionary advantage of lactose digesters, who have had the chance to reproduce more often (Swallow 2003).

7. After controlling for several explanatory variables, among others lactose intolerance, Baten and Blum (2012) reported that world region specific influences on height decrease by roughly one half.

8. The authors checked for several traits of the samples that might bias the results, such as that the samples were of prisoners, migrants, the military, or that heights were self-reported, instead of being reported by an official organization. Almost all samples were of individuals between the age 21 and 50; in case minors were included, the authors corrected for any observable bias.

9. The FAO considers food supply data as some of the most important data, since it is the basis for research on global malnutrition. It can be downloaded from FAOSTAT, the organization's database, at http://faostat.fao.org/.

10. http://go.worldbank.org/K2XOMZ5DC0.

11. One potential factor influencing animal protein consumption which has not been discussed so far is economic inequality. According to Engel's Law, relative expenditure on food decreases when per-capita income increases. Therefore, it is possible that a concentration of wealth can cause a disproportionately low consumption of animal protein. This effect, however, has been partially taken into account by controlling for infant mortality rate because the latter can also be influenced by inequality. It is also important to mention that by applying a residual analysis, researchers must be willing to take the risk of capturing measurement errors in the residuals as well. However, the data on consumption as well as the explanatory variables are of high quality. Each dataset has been compiled in order to provide comprehensive and consistent data. Moreover, it is worth having a look at the correction value time series. The only modest fluctuations during the course of time and the fact that there are no significant outliers in the panel indicate that the existence of major measurement errors is not a serious threat to the analysis. Table 5 shows that residuals of all regression models are highly correlated. Therefore, the author sticks to residuals derived in the basic regression model (model 1).

12. The reader should keep in mind that this analysis uses average male height values as a basis. Anders Gustafsson and Patrik Lindenfors (2004) have made clear that there is “no evolutionary allometric relationship between male and female stature.” Accordingly, an application of the underlying methodology to average female height may be problematic until further research has shed light on cultural and genetic influences on female stature.

13. The residuals obtained in model 1 (Table 2) serve as a basis in this step. The reason for this choice is that the residuals of all models are highly correlated (see Table 5), and model 1 is based upon the highest number of observations.

14. To be exact: 0.91 cm + 0.88 cm = 1.79 cm.